Occupant-centric control (OCC) balances energy efficiency and occupant comfort in buildings, yet methodological fragmentation hinders cumulative progress. This paper presents the Occupant-Centric Control Ontology (OCCO), a semantic framework that extends W3C/ETSI vocabularies with constructs for comfort metrics, control strategies, feedback provenance, and performance evaluation. Developed using the Linked Open Terms methodology with competency questions derived from OCC literature, OCCO unifies subjective occupant feedback with objective sensor data. Structural verification via SPARQL confirms query capability, with automated evaluation returning no remaining critical pitfalls and 88.2% FAIR compliance. OCCO provides a semantic foundation for comparable, reproducible OCC evaluation.
Building Information Modeling (BIM) requires timely as-is updates, but existing scan-to-BIM pipelines based on laser scanning or photogrammetry demand specialised hardware and offline processing, limiting frequent small-asset updates in digital-twin maintenance. This paper introduces Fast-Asset, a real-time on-site framework integrating mixed-reality capture, learning-based detection, and incremental 3D reconstruction to generate BIM-ready, pre-segmented point clouds of small MEP assets in a single walkthrough. Instant visual feedback enables in-situ validation and re-capture. Experiments demonstrate a 3-to-10x reduction in capture-to-model time versus laser scanning and photogrammetry, with comparable surface roughness and low background contamination, collapsing multi-stage offline processes into a single field operation.
Sewer systems constitute urban infrastructure critical for public health, environmental protection, and flood prevention. Decision-making in sewer systems maintenance often fails to effectively and efficiently interpret heterogeneous and multimodal inspection data into actionable maintenance strategies. To improve decision-making in sewer systems maintenance, this paper introduces a prescriptive maintenance (RxM) framework, in which a pre-trained large language model (LLM) is adapted to the domain of sewer systems using adapter-based fine-tuning. As will be shown in this paper, the LLM is capable of interpreting heterogeneous, multimodal inspection data and recommending stepwise repair procedures of sewer systems, following the paradigm of RxM.
Firefighting operations increasingly use digital tools for situational awareness, yet current systems often provide fragmented information that does not meet operational needs. Digital Twin integration appears to be a potential solution to address this gap. This study reports a survey of Irish firefighters examining information needs, data sources, Digital Twin functionality, and user readiness for adoption. The results show demand for real-time sensing and prediction, while operational decisions still rely mainly on experience and direct observation. The findings highlight the need for information prioritisation and federated rather than centralised data integration, contributing practical requirements for Digital Twin-enabled firefighting support systems.
Whole Building Life Cycle Assessments are currently hindered by manual, labour-intensive workflows and fragmented BIM and LCA data, making iterative use impractical. Existing automated workflows often lack adherence to standards, undermine data quality, or introduce data migration burdens. This study investigates integrating WBLCA data within IFC, focusing on the Danish context, by formalising Danish LCA requirements on bSDD. We propose a data model for implementing EPDs as PDTs, according to ISO 22057, in IFC using the IfcProjectLibrary exchange format defined in ISO 17549-2 and ISO 16757-5. A prototype implementation of PDT-to-IFC conversion tool demonstrates and evaluates the data model's feasibility.
Mechanical, Electrical, and Plumbing (MEP) systems require complex spatial coordination and installation sequences, making scheduling and progress monitoring challenging. This paper presents a framework that integrates Augmented Reality (AR) and Computer Vision (CV) to support MEP scheduling and progress tracking. BIM-based plans are visualized on-site through AR to guide installers, while CV algorithms detect MEP components from images to measure progress and identify schedule deviations. A prototype developed in Unity for HoloLens 2 was tested in laboratory and construction site environments. Results demonstrate its potential to improve construction managers’ situational awareness.
Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-compliant BIM from 3D point clouds. The hybrid approach combines learning-based semantic segmentation with topology-aware geometric reconstruction to model structural elements accurately. We propose vIoU, adapting voxel-based overlap evaluation to Scan-to-BIM by enabling holistic, instance-matching-free comparison of reconstructed and ground-truth models. We release the German Hospital dataset (DeKH), including high-resolution point clouds, ground truth BIMs, and semantic annotations. Experiments on DeKH and CV4AEC datasets show significant improvements over a RANSAC-based baseline, demonstrating robustness and scalability.
Digital transformation in construction asset management is advancing through BIM and emerging tools (e.g. digital twins, blockchain, and artificial intelligence), yet knowledge sharing on permitting and compliance remains fragmented. A survey of 110 professionals and 30 qualitative responses was analysed using descriptive statistics, chi‑square tests, ordinal logistic regression, and reflexive thematic analysis, then synthesised through concept mapping. Email, repositories, and collaboration platforms dominate (each at 70%), whereas 54% rarely share externally. BIM (93%) and AI (79%) exhibit the highest adoption rates. The study delivers a refined hierarchical conceptual map to support more integrated digital permit certification and compliance checking.
Disassembly and circularity assessments are limited by the lack of structured, machine-readable data on material layers and connections, which are typically represented in expert-readable 2D section drawings. This paper explores how disassembly-relevant information can be automatically derived from such drawings represented as graphs using computer vision for segmentation, vision language models for material labeling, and language-based reasoning for semantic enrichment. The prototype extracts components and their connectivity and material information and generates graph representations capturing assembly hierarchies, enabling automated disassembly reasoning. This work marks a first step toward integrating heterogeneous data sources into unified graph models for circularity assessment.
Smart Mobile Factories (SMFs) bring prefabrication to the construction site, enabling just‑in‑time delivery, shorter haulage distances and lower carbon emissions. Although integrated digital twins and modular factory designs have demonstrated the technical feasibility of SMFs in construction, their value‑creation mechanisms for operators and clients remain unclear. Adapting the Business‑Model Canvas to SMFs, we identify four emerging archetypes. Two case vignettes illustrate the transition from a low‑tech, asset‑centric to a high‑tech, data‑centric service model. Finally, we propose a mapping that relates organizational digital maturity to suitable SMF business-model archetypes, which allows to indicate a possible path toward profitable and sustainable adoption.
